{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-02T06:25:58.281235Z",
     "start_time": "2025-09-02T06:25:58.276379Z"
    }
   },
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-02T06:25:58.290312Z",
     "start_time": "2025-09-02T06:25:58.285831Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#准备数据\n",
    "X = np.array([[2,1],[3,1],[1,4],[2,6]])\n",
    "Y = np.array([0,0,1,1])"
   ],
   "id": "46212e02f3621555",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-02T06:25:58.298172Z",
     "start_time": "2025-09-02T06:25:58.291331Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#定义KNN分类模型\n",
    "knn = KNeighborsClassifier(n_neighbors=2, weights='distance')\n",
    "#模型训练\n",
    "knn.fit(X, Y)\n",
    "#预测\n",
    "x= np.array([[4,9]])\n",
    "x_class = knn.predict(x)\n",
    "print(x_class)"
   ],
   "id": "f26aa8890cf4bfe6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-02T06:25:58.389304Z",
     "start_time": "2025-09-02T06:25:58.298963Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#画图\n",
    "#使用布尔索引将两类点分开\n",
    "X1 = X[Y==0]\n",
    "X2 = X[Y==1]\n",
    "#定义不同的颜色，画两组点\n",
    "fig,ax = plt.subplots()\n",
    "ax.axis('equal')\n",
    "colors =[\"C0\",\"C1\"]\n",
    "plt.scatter(X1[:,0],X1[:,1],c=colors[0])\n",
    "plt.scatter(X2[:,0],X2[:,1],c=colors[1])\n",
    "#新点的颜色\n",
    "x_color = colors[0] if x_class == 0 else colors[1]\n",
    "plt.scatter(x[:,0],x[:,1],c=x_color)\n",
    "plt.show()"
   ],
   "id": "6a2b1e2c978faa15",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-02T06:25:58.393204Z",
     "start_time": "2025-09-02T06:25:58.389304Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "ca1a43b23279e135",
   "outputs": [],
   "execution_count": 13
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
